Salesforce Data 360 Connectors and Integrations: 2026 Guide
⏱ 6 min read
Learn how Salesforce Data 360 integrations connect CRM, databases, data warehouses, marketing platforms, enterprise applications, and other data sources to create unified, actionable customer data for analytics, automation, personalization, and AI.
Data 360 connectors bring data from Salesforce clouds and external systems into Salesforce Data 360, formerly Data Cloud. Depending on the connector, they can also share or activate data outward. Sources include CRM, cloud storage, data warehouses, marketing, commerce, and enterprise applications. Supported Zero Copy integrations let Data 360 work with selected external data without using a traditional data-copying pipeline.
This guide uses Data 360 as the current product name while retaining “Salesforce Data Cloud” where it helps readers find information about legacy terminology and existing implementations.
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Data 360 Connectors at a Glance
Data 360 connectors bring data into Salesforce Data 360 (formerly Data Cloud) from Salesforce clouds, cloud storage such as Amazon S3, data warehouses through Zero Copy (Snowflake, Databricks, BigQuery, Redshift), marketing and commerce platforms, streaming web and mobile events, and the Ingestion API or MuleSoft for other systems.
| Connector type | Examples | Data movement | Typical use |
|---|---|---|---|
| Salesforce CRM | Sales Cloud, Service Cloud, supported Industries data sources | Native connector or supported Salesforce integration | Customer, account, and case context |
| Marketing and commerce | Marketing Cloud Engagement, supported marketing and commerce sources | Connector-specific ingestion or activation | Engagement, campaign, and purchase context |
| Cloud storage | Amazon S3, Google Cloud Storage, Microsoft Azure Storage | Typically batch ingestion; selected sources also support outbound data | Files, exports, and data from ERP or legacy systems |
| Zero Copy / data federation | Snowflake, Databricks, Google BigQuery, Amazon Redshift | Query supported external data in place; specific data-sharing options vary | Warehouse data for analytics, segments, and AI use cases |
| Streaming and events | Supported web and mobile event collection, streaming sources | Streaming or near-real-time ingestion where supported | Behavioral signals and timely engagement data |
| APIs and integration platforms | Ingestion API, MuleSoft and supported connectors | API-based ingestion, batch, or streaming depending on implementation | ERP, billing, custom applications, and systems without a suitable native connector |
Connector availability, supported editions, data direction, refresh frequency, and ingestion methods can vary. Validate each source against the current Salesforce Data 360 connector catalog before implementation. Do not assume every connector is generally available or supports Zero Copy.
Salesforce Data 360 Integrations: Quick Answer
Connect
Bring Data Together
Connect Salesforce and external sources using supported connectors, APIs, integration platforms, and federation patterns.
Harmonize
Create Consistent Data
Transform and map information to a common model so data from different sources can be interpreted consistently.
Unify
Build Customer Context
Identity resolution helps match and reconcile customer records across data sources.
Activate
Put Data to Work
Use relevant data for segmentation, analytics, workflows, personalization, and supported AI experiences.
Building a Data Foundation for AI?
Before connecting more data sources, establish the architecture, governance, integration strategy, and data quality foundation required for reliable AI and automation.
What Is Salesforce Data 360?
Salesforce Data 360, formerly Salesforce Data Cloud, is Salesforce’s data platform for connecting, preparing, harmonizing, unifying, analyzing, and activating data across Salesforce and external systems.
It can work with structured and unstructured data, unify customer information, support personalization, and provide context for Agentforce and other Salesforce experiences.
Data Cloud vs. Data 360
Salesforce rebranded Data Cloud as Data 360 in October 2025. The name changed, but Salesforce states that the product functionality and content remained unchanged during the transition. Existing guides and implementations may still use the former name.
Why Are Salesforce Data 360 Integrations Important?
Enterprise data rarely lives in one application. Customer information can be distributed across CRM systems, ERP platforms, data warehouses, websites, marketing platforms, support applications, commerce systems, databases, and legacy applications.
Data 360 integrations provide ways to connect these sources so organizations can work with more complete and consistent information instead of relying on disconnected systems.
Reduce Data Silos
Connect information distributed across CRM, ERP, warehouses, applications, and other systems.
Improve Customer Context
Bring relevant customer, engagement, transaction, and behavioral information together.
Support AI
Provide trusted enterprise data and relevant context for Agentforce and other AI-powered experiences.
Activate Data
Use unified information for segmentation, workflows, analytics, personalization, and supported downstream applications.
How Do Salesforce Data 360 Integrations Work?
Data 360 integrations generally follow a lifecycle that starts with connecting data sources and continues through ingestion or federation, transformation, harmonization, identity resolution, and activation.
01
Connect
Connect Salesforce and external sources.
02
Ingest or Federate
Bring data into Data 360 or use a supported Zero Copy approach.
03
Harmonize
Transform and map information to a consistent data model.
04
Unify
Resolve identities and create unified customer profiles where applicable.
05
Activate
Use data in applications, analytics, workflows, and AI experiences.
The right method depends on the source, the required freshness of the data, security requirements, licensing, and the intended business use case. Some connectors support batch ingestion, others support streaming or Zero Copy, and some provide outbound activation or data sharing.
Types of Salesforce Data 360 Integrations
Salesforce Cloud Integrations
Connect supported Salesforce applications and CRM data to build richer customer and operational context.
Data Warehouse Integrations
Connect supported warehouse platforms for ingestion, federation, or data sharing, depending on the connector.
Database Integrations
Connect supported structured data from databases and operational systems using available connectors and integration methods.
Marketing and Commerce Integrations
Connect engagement, campaign, and transaction data to support audience segmentation, analytics, and personalization.
Zero Copy Integrations
Use supported federation or data-sharing patterns to work with data that remains in external platforms.
API and Custom Integrations
Use APIs, MuleSoft, or custom integration patterns when a native connector does not meet the requirements.
For a related example, see our guide to QuickBooks to Salesforce integration.
Salesforce Data 360 Zero Copy Integrations
Zero Copy integration allows Data 360 to work with supported external data without following a traditional approach of copying every dataset into Salesforce. Depending on the connector and configuration, data can be queried or shared across systems while remaining in the supported external platform.
Why Zero Copy matters
- Can reduce the need for traditional data duplication.
- Can help organizations use existing warehouse and lake investments.
- Can simplify selected integration architectures.
- Can provide access to external data for supported Salesforce use cases without moving every dataset into Salesforce.
- Can support analytics, activation, and AI use cases when the relevant integration is available and correctly configured.
Salesforce documents Zero Copy options for platforms including Snowflake, Databricks, Google BigQuery, and Amazon Redshift. Availability, regions, permissions, licensing, and capabilities should be confirmed for the target environment.
For a deeper comparison, read Data 360 vs. Snowflake and our guide to Snowflake and Salesforce integration.
Which Data 360 Connector Should You Use?
Choose the integration method based on where the data lives, how often it changes, whether it must be copied, and how Salesforce will use it.
Choose Zero Copy
When data already lives in a supported warehouse or lake and the use case can work with the available federation or data-sharing capabilities.
Choose Cloud Storage
When data is exchanged as scheduled files or exports from ERP, legacy applications, or other systems.
Choose the Ingestion API or MuleSoft
When no suitable native connector is available or an integration requires custom orchestration, transformation, or connectivity.
Choose Streaming
When the use case depends on timely web, mobile, or other supported event data rather than scheduled batch updates.
Do not choose a connector based on the source name alone. Confirm supported objects, data direction, refresh or event behavior, data volumes, access controls, licensing, and any current limitations.
How to Set Up a Data 360 Connector (5 Steps)
A successful implementation should start with a defined business use case and source inventory, not simply connecting every available system.
Step 01
Confirm Connector and Licensing
Check whether the source appears in the current connector catalog. Confirm edition, permissions, region, licensing, and Zero Copy requirements where relevant.
Step 02
Create the Connection
In Data 360 Setup, select the appropriate connection method and configure authentication. Use a service account with least-privilege access wherever supported.
Step 03
Create a Data Stream
Select the relevant source objects or datasets and configure the supported ingestion, streaming, or Zero Copy method and refresh behavior.
Step 04
Map and Harmonize Data
Map fields to the appropriate Customer 360 Data Model objects and define identity resolution rules where unified customer profiles are required.
Step 05
Validate and Activate
Test record counts, mappings, access, data quality, and refresh behavior in a sandbox where supported. Then activate the approved data for segments, analytics, or Agentforce grounding.
At Kizzy, we start Data 360 projects with a source inventory and the use case the data must serve, so the architecture focuses on the data business users, segments, and AI agents actually need.
Data Unification and Identity Resolution in Data 360
Connecting data is only the first step. Organizations also need to determine when records from different systems represent the same customer, account, or individual.
Data 360 supports identity resolution to match and reconcile customer identities across data sources and create more unified profiles. The quality of the results depends on source data, matching rules, and how identity attributes are configured.
Match
Identify records that may represent the same individual or organization.
Reconcile
Resolve information from different systems into a more consistent customer representation.
Unify
Create profiles that can support relevant Salesforce experiences and data-driven processes.
Salesforce Data 360 Integrations for AI and Agentforce
AI quality depends partly on the quality, relevance, and permissions of the data available to the AI system. Data 360 can provide connected and unified enterprise data for Salesforce applications and supported Agentforce use cases.
Not every Agentforce use case requires a Data 360 connector. Agents that operate on standard CRM records may be able to start with existing Salesforce data and access patterns. Connectors and additional data architecture become important when an agent needs relevant information held in external systems, such as ERP orders, warehouse data, or other enterprise applications.
Data → Context → AI → Action
Connected Data
CRM, warehouse, application, and behavioral data.
Unified Context
Relevant, harmonized, and resolved information.
AI
Data-grounded AI and Agentforce experiences.
Action
Workflows, recommendations, personalization, and permitted business actions.
For AI readiness planning, see our Agentforce readiness checklist.
Salesforce Data 360 Integration Use Cases
Customer 360
Combine CRM, engagement, transaction, service, and external data to create richer customer context.
Marketing Personalization
Build segments using harmonized customer attributes and behavioral data for more relevant experiences.
Sales Intelligence
Bring external customer and business signals into sales processes and dashboards.
Service Intelligence
Give service teams additional customer context to support case resolution and customer experiences.
Agentforce Grounding
Provide connected enterprise information that can give AI agents more relevant business context.
Data-Driven Automation
Trigger workflows and actions based on data changes, segments, insights, and business events.
Salesforce Data 360 Integration Implementation Steps
A successful implementation should begin with business objectives and data architecture rather than immediately connecting every available source.
- Define business objectives. Identify the business problem, target users, and measurable outcome the integration should support.
- Inventory data sources. Document Salesforce systems, warehouses, databases, applications, files, and other relevant sources.
- Select integration patterns. Determine whether each source should use a native connector, batch ingestion, streaming, Zero Copy, API, or another supported method.
- Map and harmonize. Transform source data and map it to the appropriate Data 360 data model.
- Configure identity resolution. Establish matching and reconciliation rules when unified customer profiles are required.
- Activate and monitor. Use the resulting data in business processes and continuously monitor quality, governance, and integration performance.
Salesforce Data 360 Integration Best Practices
- Start with high-value use cases. Prioritize integrations tied to measurable business outcomes instead of connecting every source immediately.
- Build a data inventory. Understand where critical customer, product, transaction, and behavioral information resides.
- Define data ownership. Establish authoritative sources and governance rules for important data elements.
- Prioritize data quality. Clean and standardize source information before expecting reliable unified profiles and analytics.
- Use Zero Copy where appropriate. Evaluate whether supported federation or sharing patterns can reduce unnecessary data movement.
- Design for security and governance. Account for access, privacy, compliance, consent, retention, and permitted usage.
- Test with representative data. Validate mappings, identity resolution, transformations, permissions, and downstream use cases before scaling.
- Design for AI intentionally. Identify which data sources and business context agents need instead of exposing unnecessary information.
Common Salesforce Data 360 Integration Challenges
Fragmented Data
Customer information may be distributed across systems using different formats, identifiers, and definitions.
Poor Data Quality
Duplicate, incomplete, inconsistent, or outdated source data can reduce the value of downstream unification.
Complex Data Models
Different systems may represent the same business concepts using different structures.
Identity Resolution
Organizations need reliable rules for determining when records from different systems represent the same entity.
Governance
Connected data requires clear policies for access, privacy, consent, retention, and permitted usage.
AI Readiness
AI initiatives can struggle when connected data lacks quality, context, governance, or clear business meaning.
Data 360 vs. Traditional Salesforce Data Integration
Data 360 does not replace every integration tool or application-specific connection. The right architecture may combine Data 360, MuleSoft, APIs, and other integration patterns.
Data 360 for Quote-to-Cash and Revenue Cloud
When customer, product, pricing, and transaction data must support revenue operations, integration planning should align the data architecture with the quote-to-cash process. Learn more about Salesforce Revenue Cloud implementation services.
Build a Strong Salesforce Data Foundation
Data 360 integrations are most effective when the underlying data architecture, integration patterns, governance, and AI use cases are designed together.
Explore Kizzy’s Data Foundation for AI Services
Salesforce Data 360 Integrations FAQs
What are Salesforce Data 360 connectors?
Data 360 connectors are prebuilt connections that bring data into Salesforce Data 360 from Salesforce clouds, cloud storage, data warehouses, marketing and commerce platforms, streaming web and mobile events, and other systems through the Ingestion API or MuleSoft.
What data sources can I integrate with Data 360?
You can integrate Salesforce CRM and supported Marketing Cloud data, cloud storage such as Amazon S3, warehouses such as Snowflake, Databricks, BigQuery and Redshift through supported Zero Copy options, web and mobile events, and other systems through the Ingestion API or MuleSoft. Availability and capabilities vary by connector.
What is Zero Copy in Data 360?
Zero Copy lets Data 360 query data that remains in a supported warehouse or lake, such as Snowflake, Databricks, Google BigQuery, or Amazon Redshift, instead of copying it into Salesforce through a traditional ingestion pipeline. Supported data-sharing capabilities can also make Data 360 data available to external platforms.
How do I set up a Data 360 connector?
Check the source in the connector catalog, create the connection in Data 360 Setup, create a data stream or supported federation connection, map fields to the appropriate Customer 360 Data Model objects, configure identity resolution if needed, and validate access and data quality before activating the data.
Is Salesforce Data Cloud now called Data 360?
Yes. Salesforce renamed Data Cloud to Data 360 in October 2025. The product’s functionality and content remained unchanged during the rebranding, so guides that use the former name can still be relevant. Always check current documentation for updated interface labels and capabilities.
Does Agentforce need Data 360 connectors?
Not for every use case. Agents that work only with standard CRM records may be able to use existing Salesforce data and access patterns. Connectors become important when an agent needs relevant information held in external systems, such as ERP orders or warehouse data. The architecture should be based on the agent’s actual data requirements and permissions.
Connect Your Enterprise Data With Salesforce Data 360
From Salesforce CRM and marketing data to warehouses, databases, applications, and AI use cases, the right integration architecture can turn fragmented enterprise data into actionable context.
